Prior to the manufacture and/or distribution of an electrical device (including a system or component such as a circuit board, integrated circuit, or system-on-a-chip (SOC)), the device is typically tested to determine whether it is built or functions as designed. Often, this testing is performed by automated test equipment (ATE, also called “testers”).
For the results of ATE to be meaningful, ATE needs to be calibrated. That is, the intrinsic system errors that ATE may introduce during testing under different conditions and test setups must be quantified. The data which quantifies ATE's intrinsic system errors is often referred to as “calibration data” and may comprise one or more “calibration factors”. Once generated, calibration data is used to remove ATE's intrinsic system errors from raw test data.
One way to characterize ATE's intrinsic system errors is to measure them directly. Often, this sort of calibration involves the coupling of various ATE probes to one or more known-good “calibration standards”, taking a measurement, and then comparing the measurement result to an expected measurement result.
Another way to characterize ATE's intrinsic system errors is to model them via a mathematic model. Calibration data may then be calculated from the model (although it may still be necessary to acquire certain measurements using one or more calibration standards).